머신러닝을 활용한 계획단계 도로공사 공사비용 추정 모델 개발

서지정보

ㆍ발행기관 : 한국도로학회 ㆍ수록지정보 : 한국도로학회논문집 / 25권 / 6호
ㆍ저자명 : 김제원, 김준수

목차

1. 서론
1.1. 연구 배경 및 목적
1.2. 연구 범위 및 방법
2. 선행 연구 고찰
3. 계획단계 도로공사 데이터베이스와 최적 변수선정
3.1. 데이터베이스 개요
3.2. 최적 변수 선정
4. 도로공사 공사비 추정 머신러닝 모델 개발
4.1. 머신러닝 모델 특징과 설계
4.2. 개별 모델 학습 결과
4.3. 모델별 결과 검증
5. 결론
감사의 글

영어 초록

PURPOSES : Construction cost estimates are important information for business feasibility analysis in the planning stage of road construction projects. The quality of current construction cost estimates are highly dependent on the expert's personal experience and skills to estimate the arithmetic average construction cost based on past cases, which makes construction cost estimates subjective and unreliable. An objective approach in construction cost estimation shall be developed with the use of machine learning. In this study, past cases of road projects were analyzed and a machine learning model was developed to produce a more accurate and time-efficient construction cost estimate in teh planning stage. METHODS : After conducting case analysis of 100 road construction, a database was constructed including the road construction's details, drawings, and completion reports. To improve the construction cost estimation, Mallow's Cp. BIC, Adjusted R methodology was applied to find the optimal variables. Consequently, a plannigs-stage road construction cost estimation model was developed by applying multiple regression analysis, regression tree, case-based inference model, and artificial neural network (ANN, DNN). RESULTS : The construction cost estimation model showed excellent prediction performance despite an insufficient amount of learning data. Ten cases were randomly selected from the data base and each developed machine learning model was applied to the selected cases to calculate for the error rate, which should be less than 30% to be considered as acceptable according to American Estimating Association. As a result of the analysis, the error rates of all developed machine learning models were found to be acceptable with values rangine from 17.3% to 26.0%. Among the developed models, the ANN model yielded the least error rate. CONCLUSIONS : The results of this study can help raise awareness of the importance of building a systematic database in the construction industry, which is disadvantageous in machine learning and artificial intelligence development. In addition, it is believed that it can provide basic data for research to determine the feasibility of construction projects that require a large budget, such as road projects.

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